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Role of non parity fundamentals in exchange rate determinationHo, Catherine S F; Ariff, M.
Published in:Malaysian Journal of Economic Studies
Published: 01/06/2008
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Recommended citation(APA):Ho, C. S. F., & Ariff, M. (2008). Role of non parity fundamentals in exchange rate determination: Australia andthe asia pacific region. Malaysian Journal of Economic Studies, 45(1), 45-69.
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The role of non-parity fundamentals in exchangerate determination: Australia and the Asia PacificregionMohamed AriffBond University, [email protected]
Catherine S. F. Ho
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Recommended CitationMohamed Ariff and Catherine S. F. Ho. (2008) "The role of non-parity fundamentals in exchangerate determination: Australia and the Asia Pacific region" Malaysian journal of economic studies, 45(1&2), 1-34.
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C A R F W o r k i n g P a p e r
CARF-F-125
The Role of Non-Parity Fundamentals in Exchange
Rate Determination: Australia and the Asia Pacific Region
M. Ariff
University of Tokyo, Japan, & Bond University, Australia Catherine S. F. Ho
University Technology MARA, Malaysia
August, 2008
CARF is presently supported by AIG, Bank of Tokyo-Mitsubishi UFJ, Ltd., Citigroup, Dai-ichi Mutual Life Insurance Company, Meiji Yasuda Life Insurance Company, Mizuho Financial Group, Inc., Nippon Life Insurance Company, Nomura Holdings, Inc. and Sumitomo Mitsui Banking Corporation (in alphabetical order). This financial support enables us to issue CARF Working Papers.
CARF Working Papers can be downloaded without charge from: http://www.carf.e.u-tokyo.ac.jp/workingpaper/index.cgi
Working Papers are a series of manuscripts in their draft form. They are not intended for circulation or distribution except as indicated by the author. For that reason Working Papers may not be reproduced or distributed without the written consent of the author.
The Role of Non-Parity Fundamentals in Exchange Rate
Determination: Australia and the Asia Pacific Region
M. Ariff and Catherine S. F. Ho*
University of Tokyo, Japan, & Bond University, Australia and *University
Technology MARA, Malaysia;
Mohamed Ariff Professor of Finance, Center for Advance Research in Finance University of Tokyo, Tokyo, Japan and Professor of Finance Department of Finance Bond University, QLD 4229, Australia Email: [email protected] Catherine S.F. Ho Associate Professor Faculty of Business Management University Technology MARA, 40450 Shah Alam, Malaysia. Telephone: (603) 5544-4843 Email: [email protected]
Working Paper: August, 2008
Acknowledgment: This paper is based on a study that was supported by a
funding to the first author by the University Technology MARA. The study
was completed while both authors were at the Monash University, which also
supported this research by access to the database and funding for research
support to the second author. We thank the anonymous reviewer and the
Editor of the Journal for useful comments while retaining responsibility for
remaining errors.
1
The Role of Non-Parity Fundamentals in Exchange Rate
Determination: Australia and the Asia Pacific Region
Abstract
This paper extends the literature by looking at the contribution of non-parity
variables after extracting the impact of parity variables on exchange rates of
Australia and the Asia Pacific countries. Exchange rates are examined using
high- and low-frequency multi-country panel time series data for a group of
trade-related nations in the Asia Pacific, including Japan. Our findings
suggest that exchange rate is affected by growth rate, and trade and capital
flows: other less significant variables include sovereign debt; balance of
payments; money supply; and trade openness. It also confirms that interest rate
has significant effect on exchange rates while price effect is not significant in
short run regressions. These key findings are robust across different time
intervals, thus showing new findings on the exchange rate dynamics consistent
with theories.
Keywords: exchange rates, parity theorems, trade and capital flows, foreign debt and reserves, growth, monetary and fiscal policy. JEL classifications: F31; F32; G15
1 Introduction
The motivation of this paper is to present findings on exchange rate behaviour
by including new theory/empirically-verified factors as well as other main
theory-suggested ones to investigate exchange rate determination in a trade-
related multi-country context using new research tools. One particular
unexplored factor in international finance is the role of capital flows in recent
decades in the determination of exchange rates: see Harvey (2001). There is a
need to go beyond the traditional price and interest parity factors employed to
study exchange rates of small to medium-size economies. Secondly, after
2
much effort at studying bilateral (two-country) exchange rate determination,
which has yet provided consistent findings in a vast literature of this ilk, new
approach using multi-country framework and improved research design is
needed to understand exchange rate equilibrium. That is what this paper does.
Researchers have expressed increasing frustration over their failures to
explain exchange rate movements (Dornbusch, 1987; MacDonald and Taylor,
1992). With rapid growth in trade and capital flows across national boundaries,
newer key factors are becoming noticed as affecting the value of foreign
currency (Harvey, 2001). These factors are many and include current account
deterioration, excessive foreign debt accumulation, capital flows, foreign
currency reserves and fiscal imbalances. Additional factors that are viewed
and verified in some studies as affecting exchange rate are: economic growth;
exchange rate regimes; and uncontrolled monetary expansion.
This study extends exchange literature by looking at the contributions
of non-parity variables after extracting the impact of parity variables in a first
step: for this we use a two-step regression popularised in the 1990s, and
widely used in Finance. The resulting findings provide improved
understanding of the dynamics of how exchange rates are determined in trade-
related multi-country context by using control factors beyond the traditional
parity conditions. We include countries in a trade-related group if that country
has a majority of trade with the other countries in the grouping.1 We also
provide a single country study of Australia as well.
For the multi-country Asia Pacific region as a whole, we find that the
interest rate parity holds well and this study concludes that increases in
nominal interest rates lead to downward movements in exchange rates, that is
exchange rate improves as Fisher Effect kicks in. Faster economic growth rate
3
in the region significantly facilitates the strengthening of currency values. In
addition, monetary expansions are positively related to domestic exchange
rates and this might be a reflection of faster growth rates driving monetary
expansion. From the separate findings for Australia as a comparison with a
developed country in the region, economic growth rate is the major
determinant of exchange rate movements; accumulation of international
reserves and the domestic monetary stance are also important factors in the
shorter term.
The remainder of this paper is divided into five sections. The next
section contains a brief overview of the current literature, which assisted in
identifying fundamentals relevant to this study. Section three illustrates the
methodology involved, followed by report of significant findings and
robustness testing in section four and five respectively. This paper ends with a
conclusion in section six.
2 Literature on Exchange Rate Determination
The currency exchange market is the world’s largest market in terms of daily
trading volume - in excess of US$ 2.3 trillion in 2007 - no comparison to even
the world’s combined bond or stock markets.2 The imports and exports of
goods and services, coupled with international capital flows could account for
only part of this huge currency transaction: speculative trades in currency is a
major part of this transaction. The primary function of the foreign exchange
market is to facilitate international trade and investment as well as to permit
transfers of purchasing power denominated in one currency to another.
The two parity theorems of exchange rates include the Purchasing
Power Parity (PPP of Cassel, 1918) as well as the Interest Rate Parity (IRP of
Fisher, 1930). These theorems have been extensively tested by renowned
4
scholars all over the world. Interest in currency behaviour is rekindled because
of the incompleteness of our knowledge on exchange rate determination in the
face of periodic currency crises, and by the availability of newer statistical
tools, as well as the accumulation of data over lengthy periods.
2.1 Purchasing Power Parity
PPP has been viewed by many as a basis for international comparison
of income and expenditures, an equilibrium condition; and efficient arbitrage
condition in goods as a theory of exchange rate determination. PPP established
a common ground for cross-country comparison by linking currencies of
different countries to price levels - or more precisely, price differences across
countries - as the base. The underlying theory is based on a simple goods
market arbitrage argument: ignoring tariffs, transportation costs, and assuming
common goods consumed that should ensure identical prices across countries,
under the law of one price. While this notion appears simple enough,
specifying comparative prices between two countries in the short run is
difficult. This has led to a majority of empirical literature failing to verify that
PPP holds.3
The relative version of PPP suggests that if a country’s inflation rate is
relatively higher than its trading partner’s, that country will find its currency
value falling in proportion to its relative price level increases. The exchange
rate E adjusts by k as a function of dP domestic prices and fP foreign prices.
⎟⎟⎠
⎞⎜⎜⎝
⎛= f
d
PPkE (1)
Taking the log on both sides to study changes in exchange rates, arriving at a
testable proposition, where j represents country, t represents time period, P
represents prices, d domestic and f foreign as stated below:
5
ln lnd
tjt j j jtf
t j
PE a bP
μ⎛ ⎞
= + +⎜ ⎟⎝ ⎠
(2)
In order to allow for constant price differential between baskets, the bulk of
empirical tests focused on testing relative consumption based PPP which
require that changes in the relative price levels between countries be offset by
changes in their bilateral exchange rates.
Evidence on short run PPP holding is lacking. It seems that the theory
of PPP had failed to hold.4 The apparent lack of evidence even under mostly
the current floating regimes provides researchers opportunity to revisit this
theme. It is also the same urge that led to the development of the sticky price
model of Dornbusch (1976). In the last two decades, after a number of studies
using unit root tests, researchers have still failed to reject the null hypothesis
of the random walk.5 Froot and Rogoff (1994) concluded that PPP is not a
short-run relationship: this is the basis of our research design to be explained
later to use different time intervals. Prices do not offset exchange rate swings
on a monthly or even annual basis. Frankel and Rose (1996a) examined PPP
using a panel data set of 150 countries over forty-five years and confirmed that
PPP holds and their estimate implied a half-life of PPP deviations of four
years, i.e. it is long term.
2.2 Interest Rate Parity
Interest rate parity, IRP, is the law of one price in the asset market for
securities.6 In theory, the foreign exchange market should be in equilibrium
when deposits of all currencies offer the same rate of return. A rise in interest
rates will attract more investment into the country resulting in an appreciation
of the currency in the short run and exchange rates should fall in the long run
to restore equilibrium. According to the uncovered interest rate parity, the
6
ratio of changes in exchange rate E, within a time period t, is a function of
domestic interest rate , and foreign interest rate . di fi
1 11
dt t
ft t
EE i
+ ⎛ ⎞+= ⎜ +⎝ ⎠
i⎟ (3)
While PPP implies that exchange rates will adjust to changes in
inflation differentials; International Fisher Effect (IFE) implies that relative
interest rate differentials will give rise to similar final results in exchange rates.
The ability of exchange rate markets to anticipate interest differentials is
supported by several empirical studies that indicated the long run tendency for
these differentials to offset exchange rate changes.7
2.3 Non-Parity Variables
Some researchers point out, over the last two decades, that there are
other variables which are correlated with exchange rate movements.8 These
variables could shed fresh light, and assist in identifying other-than-parity
explanations for understanding exchange rate behaviour. Despite the fact that
parity explanations have gained a centre stage up until about the 1980s for
exchange rate behaviour research, recent years have witnessed interests in
other explanations, given the conflicting empirical evidence on parity theories.
The evidence in theory and in empirical studies on these non-parity variables
are systematically examined here.
2.3.1 Current and Capital Account Deterioration
Studies of financial crises in Latin America and East Asia have been
motivated by an interest in the roles of banking, and balance of payments. The
trade and capital balances are known to be most sensitive to exchange rate
changes. For countries affected by the 1997/8 Asian financial crisis, the
7
reversal of capital flows, and current account deficits (together with high
foreign debt) have been suggested as common factors surrounding that crisis.9
Karfakis and Kim (1995) using Australian exchange rate data found
that unexpected current account deficit is associated with a depreciation of
exchange rates and a rise in interest rates. Evidence that current account
deficits reduced domestic wealth and may thus lead to overshooting of the
exchange rates thus a fall in the real value of the currency were also reported
by Obstfeld and Rogoff (1995a), Engel and Flood (1985), and Dornbusch and
Fisher (1980). There has also been a surge in international capital flows into
developing countries in the recent decades. 10 These capital flows affect
domestic output, real exchange rates, capital and current account balances for
years thereafter.11
Portfolio investment has also increased in recent years due to greater
access to capital markets via newer regulations, reduced capital controls and
the overall globalisation of financial services.12 Calvo, Izquierdo and Talvi
(2003) blamed the fall of Argentina's currency programme on their country's
vulnerability to sudden stops in capital flows. A recent study by Kim (2000)
on four countries that faced currency crises found that reversal of capital flows
as well as current account deficits are significantly related to currency crises in
these countries. 13 Rivera-Batiz and Rivera-Batiz (2001) concluded that
explosion of capital flows resulted in higher interest rates and depreciation of
exchange rates in the long run.
2.3.2 Loss of International Reserves and Excessive Foreign Currency
Debt
The amount of international reserves held by the central authority is
another factor affecting exchange rate determination.14 Due to the usage of
8
reserves as a means to defend a country’s currency, it provides credibility to
the value of the currency: this suggests that reserves and the type of currency
exchange regime in this case (managed float as a camouflage for trade
advantage) are likely to affect exchange rates.15
Calvo, Leiderman and Reinhart (1994) showed that increase in capital
inflows increase total reserves and real exchange rates of Lain American
countries. Marini and Piersanti’s (2003) study covering Asian countries found
that a rise in current and expected future budget deficits generated
appreciation in exchange rates and a decumulation of external assets, resulting
in a currency crisis when foreign reserves fell to a critical level. Hsiao and
Hsiao (2001) found a unidirectional causality from short-term external
debt/international reserves ratio to exchange rates in Korea. Similar to
Martinez (1999) on Mexico, Frankel and Rose (1996b) studied a large group
of developing countries and found that the level of debt, foreign direct
investment, foreign interest rates, foreign reserves and growth rates affect
exchange rates significantly.
2.3.3 Trade Openness, Slow Growth, Fiscal Imbalances, and Excessive
Monetary Expansion
Globalisation has resulted in domestic financial markets being slowly
more integrated with international financial markets: see Edward and Khan
(1985) and Ariff (1996). Open economies facing capital flows, competitive
interest rates and trade competition from others must lead to a defined
relationship between openness and the rate of growth in some countries.16
Similar to Karras (1999), Papell and Theodoridis’s (1998) study on openness,
exchange rates and prices found stronger evidence of PPP for countries with
9
less exchange rate volatility, and shorter distance from other countries but not
for countries with greater openness to trade.
Among the many models found in the literature to explain long-term
deviations in PPP, the most popular one is from Balassa (1964) and
Samuelson (1964). Both agued that technological progress has historically
been faster in the traded goods sector than in non-traded goods sector and
therefore traded goods productivity bias is more obvious in higher income
countries. Froot and Rogoff (1994) and Rogoff (1999) further showed that
faster growing countries would tend to experience exchange rate appreciation
relative to their slower growing partners when technological changes happen
more often in trading goods sector as a result of intense international
competition. Add to these the following: Canzoneri, Cumby and Diba (1999);
Chinn (2000); Duval (2002); and Cheung, Chinn and Pascual (2003).
MacDonald and Wojcik’s (2003) study on EU accession countries
found that productivity, as well as private and government consumption
significantly affect exchange rate behaviour. In contrast with Edwards and
Savastano (1999), Bailey, Millard and Wells (2001) found that increased
labour productivity in the US resulted in current account deficits that are
financed by large capital inflows, which appreciated the dollar exchange rates.
2.3.4 Exchange Rate Regimes
Since the breakdown of the fixed Bretton Woods system, exchange
volatility has drastically increased to levels that are beyond the explanation of
fundamentals.17 Grilli and Kaminsky (1991) concluded that real exchange
rate behaviour changes substantially across historical periods but not
necessarily across exchange rate regimes. Calvo and Reinhart (2002)
examined thirty-nine countries around the world and found that moderate to
10
large exchange rate fluctuations are very rare in managed float systems. Other
studies that found similar results includes Hasan and Wallace’s (1996), Moosa
and Al-Loughani (2003) and Edwards (2002) who explained that super-fixed
regimes were highly inflexible and inhibited adjustment process.
Hence there is literature support for checking these many non-parity
factors’ role in exchange rate determination.
3 Data, Methodology and Summary Statistics
3.1 Data
The data used relate to exchange rates between individual countries, and the
United States (U.S.) dollar (IFS line rf) as the foreign unit as observed at the
end of observation periods.18 Quarterly bilateral exchange rates for Australia
as well as nine other Asia Pacific countries are from 1974:4 to 2006:1. The
International Financial Statistics (IFS) CD-ROM is the major source for these
data. Price variables include CPI (IFS line 64) and PPI (IFS line 63) of
individual countries; T-Bill and Money market rates (IFS line 60) are used to
arrive at the interest differentials between countries. Changes in exchange
rates, prices and interest differentials are calculated using natural logarithm.
The non-parity current and capital flow variables include: trade
balance (Trade) from imports and exports of goods, and current account
balance (Cur); balance of payments (BOP) from overall balance; capital flows
include both inflows and outflows of foreign direct investment (FDI) and
portfolio investments (PT); and total reserves (TR) as well as foreign debt
(FD). Monetary expansion data is broader money19 (M2) which includes both
money and quasi-money. Growth rate (PROD) is measured by change in
Gross Domestic Product (GDP) per capita. The set of dummy variables
includes exchange regimes which are grouped into three categories: free-float,
11
exchange band/managed, and fixed regime.20 Trade openness is measured by
total trade (TTrade), that is, the sum of total imports and exports, as a
proportion of GDP: this is used to form trade-related groupings. Incomplete
data are sourced from Datastream, World Bank as well as individual country’s
Central Banks and Statistical Departments. The independent variables are
categorised into parity and non-parity variables. 21 A summary of variable
definitions and their expected signs are found in Table 1.
Insert Table 1 here.
The sample in this study includes Australia as an individual country
and a selection of nine countries in the Asia Pacific region: Australia,
Indonesia, Japan, Korea, Malaysia, New Zealand, the Philippines, Singapore
and Thailand. The developed nations include Australia, Japan, New Zealand
and Singapore and the rest are emerging economies with relatively high
growth rates. The reason behind the choice of these nine countries is the high
level of inter-trade between these countries in the same geographical region as
shown in Table 2 and the availability of information with regards to these
nations. We include these countries as each of the countries included has a
majority trade relation, that is import and exports to other countries in the
grouping is well above 50 percent of total trade. We present this as the trade-
relatedness for selecting countries to be included in a regional grouping (and
thus we had five such groups made of 54 countries across the world, although
this paper is on Asian Pacific grouping only). For related data, see Table 2: for
example, Hong Kong has a majority of its trade with the chosen group.
Insert Table 2 here
3.2 Methodology
12
The regression analysis tests the price and interest parity theorems and
then includes other non-parity fundamentals with appropriate tests to check the
robustness and validity of results. The one-step ordinary least squares model
has its limitations and hence a two-step regression is used to explain the
unexplained effects captured in the residuals from the first regression using
parity variables. Ball, Brown and Officer (1990) is a paper collected in Ball,
Brown, Finn and Officer (1990) and they popularized this procedure of
extracting theory suggested variables in the first step regression, and then
taking the residual to test further proposition. This procedure of first running a
regression, and then using the residuals from the first regression as dependent
variable on further independent variables is thus followed. We follow this
procedure as it is well-established in Finance studies. This overcomes the
problem of estimating the parity relations which have significant pair-wise
correlations with non-parity variables. Stepwise parsimonious regression
approach using the well established AIC statistics allows an examination of
each independent variable’s contribution to the model, which will be useful in
selecting a narrower set of variable. The parity and non-parity models include
different tests of the price and interest parities individually as well as jointly in
a multi-country framework.
Combined Price and Interest Parity Test
Investigating both price and interest parities should yield results that
could explain the extent to which parity hypotheses could explain changes in
exchange rates:
' ' '10 1 1* *
1ln ln ln1
tj j j jt
jt jtt jt
E P i eE P i
α α β+⎛ ⎞ +⎛ ⎞ ⎛ ⎞= + + +⎜ ⎟ ⎜ ⎟ ⎜ ⎟+⎝ ⎠ ⎝ ⎠⎝ ⎠ (4)
Non-Parity Models
13
Exchange rates are also dependent, as argued in this paper, on changes
in non-parity variables especially in the short run. This section describes the
tests aimed at estimating the individual effect such variables have on exchange
rates. These variables will also be tested together, first in a general model, and
subsequently eliminating uncorrelated variables by using the Akaike
Information Criterion (AIC) that will result in a stepwise approach.
Step 1:
Parity: ' ' '10 1 1* *
1ln ln ln1
tj j j jt
jt jtt jt
E P iE P i
α α β+⎛ ⎞ +⎛ ⎞ ⎛ ⎞= + + +⎜ ⎟ ⎜ ⎟ ⎜ ⎟+⎝ ⎠ ⎝ ⎠⎝ ⎠γ (5)
Step 2: Non-Parity:
( ) ( ) ( )' ' ' ' '40 1 2 3/ / / ( /jjt j j j j jtjt jt jt
a b Trade GDP b Cur GDP b BOP GDP b InFDI GDPγ = + + + + ) +
( ) ( ) ( )' ' ' '
85 6 7/ / / ( /jj j jjt jt jtb OtFDI GDP b InPt GDP b OtPt GDP b FD GDP+ + + ) jt +
( ) ( ) ( )' ' '
9 10 11Re / Im Pr /j j jjt jt jtb T s b odty b Bdgt GDP+ + +
'jt
( )' ' '
12 13 14/ ( / ) (Re ) ijj j jt jjtb TTrade GDP b TMy GDP b gime ν+ + + (6)
where, the dependent variable takes the residual value from the first regression.
The first regression includes the effect of the parity relations, and the residual
as the dependent variable for the second regression contains the potential
effects from non-parity relations. Thus, this two-step regression popularized
by Ball, Brown and Officer (1990) may be applied to investigate the parity and
non-parity relations both in time series – as depicted in the above – or in cross-
sectional tests.
Most researchers use monthly or annual interval data to test parity
theorems. Given the price stickiness and the evidence of long-run equilibrium
on price parity, there is need to test the relations using longer intervals of data.
14
Therefore, we tested the regression by increasing the interval period and
observing the variables across one-, two-, three- and further years so that the
tests may be conducted over longer interval data to detect the impact of
variables which has long run impacts beyond one or more years. Thus, a 2-
year window means that the variables are observed over a two-year period,
and so forth.
Common problems faced in cross-sectional and time series analysis are
non-normality of variables, non-stationarity of time series data,
multicollinearity among criterion factors, autocorrelation and
heteroscedasticity. The descriptive statistics of the variables are given in the
Appendix 1 to this paper: although not shown in the appendix, the variables
were found to be normally distributed, given the transformation. The impact of
multicollinearity is to reduce any single independent variable’s predictive
power by the extent to which it is associated with the other independent
variables. It can be detected using Variance Inflation Factor (VIF) that shows
how the variance of an estimator is inflated by the presence of
multicollinearity (Hair et al., 1998).22 Variables with larger VIF values or low
tolerance level are excluded: alternatively highly collinear variables may be
joined in some transformation of the series. Our VIF statistics show that
multicollinearity is not present in the regression: see Endnote 21 and the
Appendix 2 to this paper. As may be verified, the VIF statistics are below the
critical values, thus indicating that the multicollinearity is not likely to affect
the test statistics in the regression.
The normality of all the variables will be tested to ensure multivariate
normality and this is further ensured by specifying the variables in natural
logarithms while stationarity of the series will be tested and confirmed by
15
Augmented Dickey-Fuller (ADF) unit root test and the Kwiatkowski, Philips,
Schmidt and Shin (KPSS) Test: see Appendix 3 and also Endnote 21. The
presence of heteroscedasticity is detected by White’s test using Eviews
software: thought the test results are not reported here, we used appropriate
corrections for heteroscedasticity problem. To ensure that the assumption of
constant variance is not violated, the heteroscedasticity and autocorrelation
problems are tested and corrected using the Eviews process for this.
4. Findings
This section reports the quarterly as well as other interval results on both
Australia and the Asia Pacific region. Since the exchange rate used in this
model is against the foreign currency, a negative coefficient corresponds to an
increase in the value of domestic currency and a positive coefficient indicates
otherwise.
4.1 Short Term - Australia From the quarterly results in Table 3, it is important to note that higher
growth rate stands out clearly as the major determinant of exchange rates in
Australia. This model however, cannot find any indication of the purchasing
power and interest parity in the short term which is consistent with the current
empirical literature. Although statistically insignificant, the coefficient for
trade has the right sign where increase in trade leads to appreciation of the
domestic currency. The coefficient for foreign debt is insignificant and in the
opposite direction to theoretical prediction: it cannot be explained. Capital and
portfolio flows are insignificant but the short run coefficients have the
expected signs. It appears that for a developed country, capital and debt flows
16
do not have a significant impact on its economy and therefore its exchange
rates. That is the short-run story.
Insert Table 3 here.
Total reserves have an insignificant coefficient though the sing is
consistent with theoretical expectation that an increase in reserves raises the
confidence level others have on its currency value. Government’ fiscal budget
is insignificant but of the correct sign. This reflects that fiscal budget condition
does not drive the value of the currency. Monetary policy of the government is
also not significant (t-statistics of 1.22) where excessive monetary expansion
leads to deterioration of currency value. The total trade or trade openness
coefficient is insignificant and the relationship is negative. The sign reflects
that openness to trade resulted in huge imports that send the currency value
falling. The F-probability of 0.000 shows that the model is statistically
significant and that growth rate is the major driving force behind exchange
rate movements. The adjusted R-squared of 0.873 also indicates that more
than 80 per cent of the movement in exchange rates can be explained by this
model.
4.2 Short Term – Asia Pacific
For the region as a whole in Table 3, interest and price parities do not
hold in the short run as these are statistically insignificant in the short run
consistent with current literature. The coefficient for growth rate of -57.30 is
highly statistically significant (t-stats -13.06) with major effect on exchange
rates as well as in the expected direction. Improvement in the balance of
payments leads to an increase in the value of domestic currency in the region;
nonetheless it is only marginally significant with t-statistics of -1.77. It is most
interesting to note that domestic monetary expansion is significant and directly
17
related to the value of the currency. This is not consistent with the monetarists’
model and might be a reflection of rapid growth of the region driving
monetary expansion. The coefficient for foreign debt of 0.162 is statistically
significant (t-statistics 1.83) and shows that increase in sovereign debt
negatively impacting domestic exchange rates. Government’s fiscal budget is
another significant determinant of exchange rates for Asia Pacific countries in
the short term where improvement in the budget balance improves the
exchange rate performance too.
The capital, portfolio flows, and monetary regime are generally
insignificant and this shows that short run quarterly changes in these variables
do not have a strong impact on exchange rates. Total trade of the region is also
significant but is inversely related to exchange rates. This might be explained
by the large amount of imports absorbed into the region when these countries
accumulated wealth through rapid growth which is supported by huge exports
too. Thus trade openness allows these countries to import more productive
technologies and it facilities to enable them to sustain higher and continuous
growth. With adjusted R-squared of 0.738 for a region of nine countries, the
model can explain more than 70 per cent of changes in exchange rates.
Short Term - Parsimonious Model
The parsimonious model in Table 4 indicates similar findings for the
region and confirms the significance of growth rates and others have on
exchange rates. These results are more reliable. Sensitivity analysis of the tests
is conducted with no substantial differences to the reported results.
Parsimonious model indicates that the coefficients for the five major factors
for the region of Asia Pacific countries are namely (1) growth rates, (2)
balance of payment, (3) budget balance, (4) foreign debt accumulation and (5)
18
total money, continue to be statistically significant which is consistent with
theories and some studies. The other variables are not significant and some of
them have incorrect sign.
Insert Table 4 here
4.3 Longer Term - Australia
The longer term results are shown in Table 5 as a comparison from
short to longer period of time. It is crucial to note that purchasing power parity
is achieved at two years for Australia, which is not only statistically significant
(t-statistics 3.71) and of correct sign. Sample sizes are too small to allow
reliable use of parity fundamentals to predict changes in exchange rates, even
when these fundamentals do determine exchange rates. Further study with
longer time series in the future would obtain more significant results.
Insert Table 5 here
There is without doubt growth rate is the major determinant of
exchange rates in Australia from the results obtained here for quarterly as well
as all subsequent time periods. Fiscal stance is also an important longer term
variable, as fiscal expansion is inversely associated with the movement of
exchange rates and is statistically significant in both one- and three- yearly
regressions. Monetary expansion is becoming more important in the longer
term and the results correspond to theory; it is indirectly related to exchange
rates. It is important to note that exchange rate appreciation of a certain
magnitude always become more worrisome if coupled with excessive
monetary expansion.
The coefficients of foreign debt of 2.219 and 1.951 are significant (t-
statistics 2.90 and 3.73 respectively) and of the correct direction for one, and
two year intervals. This shows that in the longer-term, persistent accumulation
19
of foreign debt decreases the value of the currency when investors lose
confidence in the ability of the country to repay its foreign debt. Moreover,
high foreign debt leads to further deterioration of the economy if accompanied
by high world interest rates.
Non-parity variables which are marginally significant in affecting
exchange rates in the shorter time period also include inflow of foreign direct
and portfolio investments which are positively related to exchange rates in the
one yearly interval. This shows that in the longer term, inflows of investment
increase the value of the Australian currency and likewise for accumulation of
balance in the current account. The R-squared for the all the period intervals
are above 80 per cent and this shows that the models can explain a large
portion of exchange rate movements. In summary, the key driving force
behind the Australian currency is still growth rates in the domestic economy.
Other variables that affect exchange rates include inflow of foreign direct and
portfolio investments, foreign debt accumulation, total trade, and monetary
and fiscal stance of the government.
Parsimonious Model
The results from a parsimonious model given in Table 6 clearly show
that growth rate is the major determinant of exchange rates in Australia. It is
interesting to note that accumulation of reserves and portfolio outflows are
also significant determinants for one and two yearly interval, respectively.
Insert Table 6 here.
4.4 Longer Term - Asia Pacific
Consistent with theoretical position, interest parity theory is
consistently holding in the region as a whole as it is statistically significant in
20
all the yearly intervals in Table 7. Price parity however, is not being explained
in the model here. It might be, since these countries are relatively new with
data of only about thirty years, longer time period and longer series of data
once available in the future, would enable more significant results to be
determined using our intervalling method to control sticky price.
Insert Table 7 here
The coefficient for growth rates, interest rates and total money are
statistically significant in four year interval and are the major forces behind
exchange rates in the region. The growth of countries in this region helps to
explain the increase in currency value throughout the thirty years of the study.
The balance of payment is only a shorter term variable in determining
exchange rates in the region as accumulation of the balance is reflected in an
improvement in exchange rates as predicted. Monetary expansion in the region
is positively related to exchange rates and trade openness is negatively related
to exchange rates as explained in the earlier section.
Although significant only in the shorter term, the coefficient on foreign
debt of 0.262 at four year interval is in the expected direction in the longer
term. Other capital and portfolio flows, current account, and trade flows are
insignificant in affecting exchange rates which is surprising because it is
believed that they are the major reasons for the financial crisis in the region in
1997. This might be due to the insufficient length of time series available from
these countries when some of them only started recording these flows in the
late nineties.
The parsimonious result in Table 8 reinforces the findings from Table
7. Overall, the adjusted R-squared are all above 75 per cent which shows that
21
the models can explain a high proportion of changes in exchange rate in the
region. On top of that, the F- probabilities for the models are very significant.
Insert Table 8 here
5 Robustness Testing
This paper undertakes robustness tests to reaffirm the results. We first remove
statistically insignificant variables from the model to form the parsimonious
model. The results are robust to the removal of these variables, with all
explanatory variables and significance of coefficient persisting. On top of that,
using stepwise approach reconfirms the existing results, as seen above.
We also removed relatively highly correlated variables from the model
(despite the VIF tests) and re-ran the analysis. The results largely persisted.
The outflows of capital and portfolio figures are generally not significant
anyway and when they are removed, the number of observations increases and
adjusted R-squared also increases. Overall, the F- statistics of the models
improved and the models are statistically significant.
6 Conclusion
This study reports new findings, if accepted, may extend the existing currency
literature by considering the extent to which both parity and non-parity
variables influence exchange rates in Australia and also in a region of nine
closely-trading countries in the Asia Pacific. We find that of the non-parity
variables for Australia, three have extensive explanatory power in the models
investigated in this paper: (1) growth rates, (2) foreign debt and (3) fiscal
expansion. Collectively, these variables explain about 80 per cent of the
changes in exchange rates in Australia. The parity variables, on the other hand,
are generally statistically insignificant. Two other non-parity variables which
22
23
are marginal, thus are not significant, include (4) monetary expansion and (5)
inflow of foreign capital.
The major driving forces behind exchange rates in the Asia Pacific
region as a whole are (1) interest rates, (2) growth rates and (3) monetary
expansion. (4) Interest parity is consistently holding in the region for all
intervals of time periods. (5) Price parity however, needs further extension of
data and tests in a future study due to insufficient data available for these
relatively new countries: as mentioned earlier, we could not extend the tests
beyond 4-year intervals, which is too short to reveal the price parity
equilibrium given sticky prices. Other minor non-parity variables that appear
to be statistically borne out are identified: (6) balance of payments, (7)
government’s fiscal balance and trade openness as these factors are not always
significant in all regressions. The explanatory power of models is also large.
It is important to note that different countries face different set of
parity and non-parity variables which are significant in driving their exchange
rates. The results for parity and non-parity variables are robust to alternative
specifications of the model. We believe the tests developed in this study has
led to improved results, and help identify new variables that are related to
exchange rates while the puzzle of short-term versus long term behaviour is
made obvious by applying different interval period. It is to be noted that the
use of different intervalling periods beyond the monthly and quarterly
intervals used by most researchers enabled us to bring in the impact of long-
cycle sticky price effect. Finally, this study ventured to include factors
suggested by theories/empirical reports to identify non-parity variables, which
appear to be very significant contributors to the exchange rate determination.
24
Notes: 1 We grouped 54 countries into 5 trade-related groupings, the Asia pacific countries is one of
the five groupings thus formed.
2 The Economist. April, 2001. Forex is 50 times larger by volume than the equity market as
stated by Euromoney.com. Resnick, Bruce G. Business Horizons, Nov/Dec 89, Vol 32, Issue
6 and updated by others. 3 Empirical work that has led to conflicting empirical findings for PPP includes MacDonald
(1993), Rogoff (1996), Edison, Gragnon and Melick (1997), Cheng (1999) and Bayoumi and
MacDonald (1999). They have all found no clear evidence or at best, very weak relationship
between inflation and exchange rates. 4 Henry and Olekaln’s (2002) study on Australia found little evidence for long run equilibrium
between exchange rate and prices. In a similar view, Adler and Lehman (1983) found that the
deviations from PPP follow a random walk without reverting back to PPP for 43 countries. 5 MacDonald and Ricci (2001), Kuo and Mikkola (2001), Lothian and Taylor (2000), Mark
and Sul (2001), Schnabl and Baur (2002) found considerable evidence for long run relation
and concluded that fundamentals paly a significant role in determining exchange rates. 6 The interest rate theory was first developed by Keynes (1923) and Fisher (1930) through the
introduction of Fisher effect for domestic interest rate theory. 7 Studies that provided evidence include Mark (1995), Chortareas and Driver (2001), Chinn
and Meredith (2002), Hoffman and MacDonald (2003) which found measures of long run
expected changes in exchange rates highly correlated with interest rate differentials. 8 Frankel and Rose (1996b) on current account and government budget deficits; Calvo,
Leiderman and Reinhart (1994) on capital flows, inflation and current account deficits; and
Aizenman and Marion (2002) on reserve and credibility; and many others. 9 It is documented that the recent currency crises were due to vast changes in these variables,
including Kim (2000). 10 Gross foreign direct investment as a percentage of GDP increased more than 100 percent for
Korea, the Philippines and Indonesia for the period 1990-2001.Net private capital flows into
six developing regions in the world totalled US$167.976 millions in 2001. Source: 2003
World Development Indicators, database, World Bank, 13 April 2003. 11 Studies on capital flows that affect output, exchange rates and balance of payments include
Kim (2000) and Calvo and Reinhart (1999). 12 Portfolio investment inflows have increased from RM19,346 millions in 1991 to a peak of
RM238,454 millions in 1994 for Malaysia. Source: Bank Negara Malaysia and Department of
Statistics, Malaysia. Portfolio investment averaged US102 billion for 1995-96 and US26
billion for 1997-2000 according to World Economic Outlook, 2003, IMF. 13 Using annual data for 21 OECD countries, Krol (1996) found that capital flows have
significant effect on current accounts as well as exchange rates and this is reinforced by Kim
(2000).
25
14 Korea’s usable reserve fell from US$28 billion to a mere US$6 billion when their currency
went on a free fall in December 1997: Aizerman and Marion (2002). Brazil’s reserves fell
from US$75 billion to less than half of that before the currency collapsed in 1998: Dornbusch
and Fisher (2003). 15 Total external debt for six developing regions in the world according to World Bank
classification amounted to US$2,332,621 millions for 2001. Source: 2003 World Development
Indicators, World Bank. 16 Karras and Song (1996) investigated 24 OECD countries for thirty years and found positive
relationship between output volatility, economy’s trade openness and exchange rate flexibility. 17 Reviewing the US experience with flexible exchange rates, Dornbusch (1987b) found that
changes in exchange rates in the last fifteen years are inconsistent with any explanations in
theory and may not be related to fundamentals. 18 These exchange rate quotations can be expressed in either a unit of foreign currency (Direct
quote) or a local unit expressed in foreign equivalent (Indirect quote). A direct exchange rate
quotation gives the home currency price of in terms of foreign currency whereas the indirect
quote gives the one unit home currency equivalent in foreign currency. They are actually the
reciprocal of each other. In order to avoid confusion, direct quotations are used, as is the
practice in the literature, in this study unless stated otherwise. 19 IFS defined money as the sum of currency outside deposit money banks and demand
deposits, and quasi money as the sum of time, savings and foreign currency deposits of
resident sector. 20 Exchange regimes are according to Reinhart and Rogoff (2002). 21 In order to minimize multicollinearity effects, all parity variables were transformed as first
difference and specified in the models as natural logarithm. Further investigation of the
variables indicated that there is no significant correlation among the independent variables
using VIF tests. 22 These test results are shown in this paper in the Appendix 2 on more than the variables
included in this study: we did the tests with the methods described in this paper and other
methods used to study the other four regions.
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Table 1: Summary of Variables and Definitions
No. Variable Definition Expected Sign 1. LnER Log difference of Exchange Rate over time periods 2. LnP Log difference of Prices over time periods + 3. LnI Log difference of Interest Rate over time periods + 4. Trade/GDP Trade Balance / Gross Domestic Product (GDP) - 5. Cur/GDP Current balance / GDP - 6. BOP/GDP Balance of Payment / GDP - 7. TRes/M Total Reserve / Total Import - 8. FD/GDP Foreign Debt / GDP + 9. InFDI/GDP Inflows of Foreign Direct Investment / GDP - 10. OutFDI/GDP Outflows of Foreign Direct Investment / GDP - 11. InPt/GDP Inflows of Portfolio Investment / GDP - 12. OutPt/GDP Outflows of Portfolio Investment / GDP - 13. Bdgt/GDP Budget Deficit or Surplus /GDP - 14. TMy/GDP Total Money (M2) / GDP + 15. Prodty Gross Domestic Product / Total Population - 16. TTrade/GDP Total Exports and Imports / GDP - 17. Regime Exchange Regime +/-
Table 2: Proportion of trade between countries in the Asia Pacific Region
29
Country ASEAN Japan EU &
UK
US Korea China Hong Kong and Taiwan
Australia/ New
Zealand Australia 14% 19% 12% 10% 8% 7% 4% 7% Indonesia 14% 21% 13% 7% 5% 4% 5% Japan 6% 30% 7% 10% 12% 5% Korea 10% 20% 15% 6% 19% Malaysia 25% 11% 21% 5% 7% 5% 7% New Zealand 12% 10% 16% 5% 5% 20% Philippines 12% 15% 10% 26% 4% 7% 12% 2% Singapore 25% 7% 6% 15% 4% 6% 15% 3% Thailand 15% 15% 20% 12% 5% 5% 9%
Source: CIA Factbook 2005.
Table 3: Quarterly One and Two-Step Results Region Australia Asia Pacific 2 Step 2 Step Intercept -.002
(-0.16) .002
(0.30) .019
(4.20)* .008
(2.59)* Price -.035
(-2.59)* -.036
(-1.15) -.005
(-0.76) -.018
(-2.50)* Parity
Interest .001 (0.01)
-.111 (-0.81)
.054 (1.21)
.163 (1.22)
Trade/GDP -1.927 (-1.42)
-1.234 (-0.94)
.066 (0.47)
.076 (0.54)
BOP/GDP .086 (0.26)
.035 (0.11)
-.139 (-1.77)***
-.154 (-1.91)***
Cur/GDP 1.341 (1.22)
1.009 (0.94)
.073 (0.62)
.059 (0.52)
InFDI/GDP .220 (0.71)
.202 (0.64)
-.001 (-0.01)
.007 (0.14)
OtFDI/GDP -.070 (-0.17)
-.130 (-0.29)
.003 (0.03)
.017 (0.17)
InPt/GDP -.015 (-0.07)
.019 (0.09)
-.037 (-0.61)
-.029 (-0.47)
OtPt/GDP -.504 (-1.34)
-.580 (-1.58)
.007 (0.04)
.014 (0.07)
TRes/Im -.033 (-1.61)
-.030 (-1.53)
.001 (0.03)
.003 (0.12)
ForDt/GDP -14.372 (-0.08)
133.894 (0.79)
.162 (1.83)***
.159 (1.75)***
Prodty -.060 (-16.87)*
-.061 (-17.19)*
-57.30 (-13.06)*
-56.10 (-13.45)*
Bdgt/GDP 16.227 (0.14)
-12.793 (-0.10)
-.112 (-3.17)*
-.102 (-2.81)*
TMy/GDP .144 (1.22)
.149 (1.23)
-.261 (-10.17)*
-.257 (-9.99)*
Regime .007 (1.26)
.004 (1.34)
-.001 (-0.29)
.001 (0.32)
Non-Parity
TTrade/GDP .249 (0.40)
.124 (0.20)
.056 (1.43)
.071 (1.65)
Adj R² .873 .867 .738 .729 F-prob 0.000 0.000 0.000 0.000
*, ** and *** denote statistical significance at 1, 5 and 10% levels. t-statistics are in parentheses. The results were corrected for heteroscedasticity and autocorrelation using Newey-West HAC matrix.
Table 4: Quarterly One and Two-Step Parsimonious Results
30
Variables: Australia 2 Step Asia Pacific 2 Step
Prodty -.061 (-28.52)*
-.061 (-27.66)
-57.275 (-14.80)*
-55.637 (-13.83)*
TMy/GDP -.265 (-25.91)*
-.259 (-24.64)*
Bdgt/GDP -.141 (-3.86)*
-.136 (-3.53)*
ForDebt/GDP .172 (2.74)*
.167 (2.58)*
BOP/GDP -.138 (-4.44)*
-.159 (-4.94)*
LNPPI -.018 (-2.39)*
Adjusted R² .878 .870 .738 .730 F-Probability 0.000 0.000 0.000 0.000 *, ** and *** denote statistical significance at 1, 5 and 10% levels. t-statistics are in parentheses.
Table 5: Results from Quarter, One to Four Yearly - Australia
Australia Quarterly One
Yearly Two
Yearly Three Yearly
Four Yearly
Intercept -.002 (-0.16)
.055 (0.77)
.165 (5.90)**
.284 (5.39)**
.202 (2.22)
Price -.035 (-2.59)*
.041 (0.50)
.256 (3.71)*
-.089 (-1.24)
-.214 (-1.68)
Parity
Interest .001 (0.01)
-1.631 (-3.75)**
-2.785 (-2.32)
-.966 (-1.37)
1.196 (0.53)
Trade/GDP -1.927 (-1.42)
1.501 (0.72)
5.339 (1.72)
BOP/GDP .086 (0.26)
.680 (0.63)
Cur/GDP 1.341 (1.22)
-2.277 (-1.38)
InFDI/GDP .220 (0.71)
-3.647 (-2.78)***
OtFDI/GDP -.070 (-0.17)
.038 (0.03)
InPt/GDP -.015 (-0.07)
-1.412 (-1.04)
OtPt/GDP -.504 (-1.34)
.130 (0.09)
.736 (0.12)
TRes/Im -.033 (-1.61)
-.022 (-0.12)
1.687 (5.59)**
ForDt/GDP -14.372 (-0.08)
2.219 (2.90)***
1.951 (3.73)***
Prodty -.060 (-16.87)*
-103.681 (-4.23)*
-130.013 (-6.64)**
-70.821 (-5.54)**
-57.880 (-6.30)***
Bdgt/GDP 16.227 (0.14)
2.472 (3.73)**
-1.088 (-1.90)
2.657 (3.31)***
TMy/GDP .144 (1.22)
.537 (1.01)
.917 (1.96)
Regime .007 (1.26)
.021 (0.80)
Non-Parity
TTrade/GDP .249 (0.40)
-.169 (-0.54)
Adj R² .873 .903 .973 .810 .947 F-prob 0.000 0.031 0.021 0.130 0.158
*, ** and *** denote statistical significance at 1, 5 and 10% levels. t-statistics are in parentheses. The results were corrected for heteroscedasticity and autocorrelation using Newey-West HAC matrix. ªWith the length of time period increasing to three and four yearly intervals, one shortcoming of the data set is that the number of observations falls. However, with longer time period available in the future, this problem can be eliminated.
Table 6: 1 – 4 Yearly Parsimonious Result - Australia
Quarterly One Yearly
Two Yearly
Three Yearly
Four Yearly
31
Prodty -.061
(-28.52)* -60.190
(-10.33)* -83.356
(-14.38)* -84.503
(-20.54)* -55.487 (-4.47)*
LNPPI -.018 (-2.39)*
TRes/Im 1.220 (5.52)*
Regime .086 (3.54)*
PtOt 17.021 (9.59)*
Adj R² .878 .848 .966 .991 .776 F-prob 0.000 0.000 0.000 0.000 .005
*, ** and *** denote statistical significance at 1, 5 and 10% levels. t-statistics are in parentheses.
Table 7: Results from Quarter, One to Four Year – Asia Pacific
Asia Pacific Quarterly One Yearly Two Yearly Three Yearly Four YearlyIntercept .019
(4.20)* .077
(6.39)* .062
(1.72)*** .118
(1.59) .048
(0.53) Price -.005
(-0.76) -.002
(-0.18) -.006
(-0.20) -.064
(-1.21) -.198
(-2.65)** Parity
Interest .054 (1.21)
.169 (1.87)***
.763 (4.06)*
.681 (1.76)***
2.190 (5.40)*
Trade/GDP .066 (0.47)
-.003 (-0.02)
-.011 (-0.05)
.708 (1.56)
.811 (2.33)**
BOP/GDP -.139 (-1.77)***
-.389 (-1.87)***
-.043 (-0.18)
-.183 (-0.28)
-.201 (-0.32)
Cur/GDP .073 (0.62)
-.010 (-0.07)
.288 (1.44)
-.065 (-0.16)
.052 (0.12)
InFDI/GDP -.001 (-0.01)
.235 (1.00)
.238 (0.37)
1.688 (2.22)**
.372 (0.37)
OtFDI/GDP .003 (0.03)
.507 (1.33)
0.420 (0.61)
.431 (0.51)
1.608 (0.75)
InPt/GDP -.037 (-0.61)
.048 (0.30)
-.384 (-0.95)
.906 (1.13)
1.276 (0.81)
OtPt/GDP .007 (0.04)
.210 (0.76)
-.071 (-0.09)
-.507 (-0.55)
.103 (0.05)
TRes/Im .001 (0.03)
.017 (0.21)
-.035 (-0.22)
-.033 (-0.11)
-.005 (-0.03)
ForDt/GDP .162 (1.83)***
-.205 (-1.18)
-.128 (-0.56)
-.310 (-1.77)***
.262 (0.74)
Prodty -57.30 (-13.06)*
-29.099 (-4.62)*
-29.718 (-5.33)*
-30.728 (-5.82)*
-31.701 (-3.06)*
Bdgt/GDP -.112 (-3.17)*
.313 (1.45)
.212 (0.43)
.561 (0.72)
-.385 (-0.78)
TMy/GDP -.261 (-10.17)*
-.770 (-10.60)*
-.510 (-3.78)*
-.696 (-3.08)*
-.605 (-3.48)*
Regime -.001 (-0.29)
.001 (0.10)
.040 (1.79)***
.058 (1.37)
.064 (1.14)
Non-Parity
TTrade/GDP ..56 (1.43)
.047 (1.70)***
.028 (0.79)
.017 (0.38)
.204 (4.06)*
Adj R² .738 .873 .788 .793 .830 F-prob 0.000 0.000 0.000 0.000 0.000
*, ** and *** denote statistical significance at 1, 5 and 10% levels. t-statistics are in parentheses. The results were corrected for heteroscedasticity and autocorrelation using Newey-West HAC matrix.
Table 8: 1 – 4 Yearly Parsimonious Result – Asia Pacific
Quarterly One Yearly Two Yearly Three Yearly Four Yearly Prodty -57.275
(-14.80)* -31.025 (-7.99)*
-28.062 (-5.80)*
-27.827 (-4.66)*
-33.709 (-4.79)*
LnI .708 (3.23)*
1.886 (4.22)*
32
TTrade .037
(2.13)**
TMy -.265 (-25.91)*
-.732 (-14.01)*
-.511 (-5.99)*
-.593 (-4.95)*
-.446 (-2.88)*
ForDebt/GDP .172 (2.74)*
BOP/GDP -.138 (-4.44)*
-.419 (-4.26)*
Bdgt/GDP -.141 (-3.86)*
.435 (2.82)*
Cur/GDP .385 (2.63)*
.470 (2.25)**
.558 (2.53)**
Adj R² .738 .876 .808 .786 .823 F-prob 0.000 0.000 0.000 0.000 0.000
*, ** and *** denote statistical significance at 1, 5 and 10% levels. t-statistics are in parentheses.
Appendix 1a: Descriptive Statistics on Exchange Rates, Price and Interest Differences of Asia-Pacific Countries
ln change in exchange rates ln change in price differences ln change in interest differences
Country Mean/ Median
Std Dev Max/ Min
Mean/ Median
Std Dev Max/ Min
Mean/ Median
Std Dev Max/ Min
Australia 0.008/ 0.001
0.052 0.156/ -0.097
-0.079/ -0.045
0.111 0.051/ -0.385
0.033/ 0.024
0.029 0.097/ -0.002
Indonesia 0.035/ 0.011
0.132 0.582/ -0.331
-0.266/ -0.358
0.640 1.101/ -1.272
0.083/ 0.065
0.094 0.507/ -0.010
Japan -0.004/ 0.007
0.066 0.150/ -0.169
0.018/ 0.018
0.053 0.154/ -0.124
-0.025/ -0.028
0.022 0.025/ -0.070
Korea 0.011/ 0.008
0.078 0.617/ -0.203
-0.005/ -0.027
0.084 0.191/ -0.223
0.050/ 0.044
0.036 0.165/ -0.008
Malaysia 0.008/ 0.000
0.044 0.236/ -0.063
-0.021/ -0.023
0.039 0.151/ -0.085
-0.007/ -0.004
0.036 0.058/ -0.097
Philippines 0.016/ 0.003
0.055 0.250/ -0.115
-0.092/ -0.072
0.243 0.287/ -0.523
0.082/ 0.074
0.035 0.168/ 0.028
Singapore -0.002/ -0.005
0.027 0.094/ -0.068
-0.015/ -0.008
0.028 0.068/ -0.080
-0.016/ -0.017
0.011 0.013/ -0.049
Thailand 0.009/ -0.001
0.067 0.348/ -0.197
-0.031/ -0.045
0.114 0.199/ -0.215
0.028/ 0.028
0.038 0.138/ -0.041
Appendix 1b: Asia Pacific - Descriptive Statistics of Non-Parity Variables
Variables N Mean Median Std Dev Max/Min
33
1 Trade/GDP 879 .0072 .0004 .0745 .6307/-.5523 2 Cur/GDP 794 .0009 .0002 .0306 .1391/-.1717 3 BOP/GDP 789 .0013 .0003 .0587 .4504/-.3229 4 InFDI/GDP 825 .0003 .0001 .0289 .4107/-.3096 5 OutFDI/GDP 686 .0001 .0000 .0099 .1376/-.0956 6 InPt/GDP 794 .0003 .0000 .0544 .4446/-.5504 7 OutPt/GDP 592 .0004 .0000 .0446 .3822/-.4805 8 TRes/M 934 .0351 .0278 .1200 .7341/-.5147 9 Bdgt/GDP 852 .0006 .0011 .0675 .3080/-.3067 10 TMy/GDP 997 .0429 .0395 .1505 1.2355/-1.2637 11 Prodty 994 .0001 .0001 .0006 .0052/-.0060 12 FD/GDP 753 .0035 .0002 .0265 .2123/-.2789 13 TTrade/GDP 903 .0177 .0043 .0887 .6307/-.5523
Appendix 2: Parity and Non-Parity Variables VIF and Tolerance
Measure
G-10 Asia Pacific Latin America Eastern Europe ASEAN Variables VIF Tolerance VIF Tolerance VIF Tolerance VIF Tolerance VIF ToleranceLNP 1.849 0.541 1.050 0.952 1.302 0.768 1.679 0.596 1.018 0.982 LNI 1.253 0.798 1.056 0.947 1.280 0.781 1.807 0.553 1.065 0.939 Trade/GDP 3.351 0.298 3.000 0.333 6.691 0.149 3.873 0.258 2.993 0.334 Cur/GDP 3.319 0.301 2.944 0.340 7.730 0.129 3.863 0.259 3.080 0.325 BOP/GDP 1.536 0.651 1.564 0.640 7.275 0.158 2.796 0.358 1.649 0.606 InFDI/GDP 1.629 0.614 1.350 0.741 1.088 0.919 1.184 0.845 1.100 0.909 OutFDI/GDP 1.660 0.603 1.593 0.628 1.097 0.911 1.096 0.912 1.140 0.877 InPt/GDP 1.154 0.867 1.841 0.543 5.838 0.163 1.992 0.502 0.327 0.754 OtPt/GDP 1.099 0.910 1.175 0.851 1.249 0.800 1.234 0.811 1.144 0.874 TRes/IM 1.570 0.637 1.445 0.692 1.245 0.803 2.061 0.485 1.474 0.678 Bgt/GDP 1.157 0.864 1.173 0.852 1.271 0.787 1.331 0.751 1.095 0.913 TMy/GDP 1.344 0.744 1.282 0.780 1.448 0.691 1.961 0.510 3.340 0.299 PROD 2.178 0.459 1.133 0.882 1.091 0.916 2.052 0.487 3.226 0.310 FD/GDP 1.230 0.813 1.143 0.875 1.197 0.836 1.382 0.724 1.205 0.830 TTrade/GDP 1.838 0.544 1.266 0.790 1.481 0.675 1.731 0.578 1.321 0.757 Regime 1.649 0.606 1.155 0.866 1.184 0.845 1.587 0.630 1.170 0.855
* VIF values of more than 10 shows significant multicollinearity.
Appendix 3a: Unit Root Tests for Parity Variables in Asia Pacific
ADF Test KPSS Test
34
ln change in exchange rate
ln change in price differences
ln change in interest differences
ln change in exchange rate
ln change in price differences
ln change in interest differences
t-stats Model(lag)
t-stats Model (lag)
t-stats Model (lag)
KPSS statistic
KPSS statistic
KPSS statistic
Australia -9.05*** C(0) -4.60*** C(0) -2.72 C&T(1) 0.103 0.903*** 0.106 Indonesia -8.13*** C(0) -1.69 C&T(1) -4.13*** C&T(2) 0.058 0.193** 0.054 Japan -8.61*** C(0) -2.24 C&T(0) -1.80 C(6) 0.135 0.092 0.255 Korea -11.41*** C(0) -2.21 C&T(0) -3.51** C(1) 0.098 0.216*** 0.173 Malaysia -2.08 C(2) -0.02 C(0) -3.50** C&T(0) 0.261 0.304 0.075 Philippines -7.03*** C(0) -2.60 C&T(1) -2.76* C(0) 0.108 0.112 0.343 Singapore -9.98*** C(0) -2.90** C(0) -4.56*** C(1) 0.306 0.448* 0.097 Thailand -8.31*** C(0) -3.22* C&T(1) -2.36 C(0) 0.109 0.079 0.150 Pooled -22.8*** C(0) -3.29 C(0) -5.23** C(0) 0.201 0.169 0.173
Critical values for ADF tests at 10,5 and 1% levels of significance are respectively, -2.59, -2.90 and –3.53 with a constant and –3.17, -3.48 and –4.09 with a constant and a deterministic trend. Critical values for KPSS tests at 10,5 and 1% levels of significance are respectively, 0.35, 0.46 and 0.74 with a constant and 0.12, 0.15 and 0.22 with a constant and a linear trend. Note: For the ADF tests, the unit root null is rejected if the value of the DF t-statistics is less than the critical value. For the KPSS tests, the null of stationarity is rejected if the value of the KPSS statistic is greater than the critical value. *, ** and *** denote statistical significance at 10, 5 and 1% level. The critical values for the ADF tests are from MacKinnon (1991).
Appendix 3b: Unit Root Tests for Parity and Non-Parity Variables in (G-
10 countries) and Asia Pacific Region
G-10 Asia Pacific Variables ADF Test KPSS Test ADF Test KPSS Test t-stats Model
(lag) KPSS statistic t-stats Model
(lag) KPSS statistic
lnER -14.71*** C(13) 0.035 -6.72*** C(0) 0.772*** lnP -3.53*** C(0) 0.114 -3.16*** None 0.119 lnI -6.50*** C(0) 0.203 -7.26*** C(0) 0.111 Trade/GDP -8.98*** C(11) 0.202 -6.31*** C(19) 0.494** Cur/GDP -14.68*** C(6) 0.099 -7.24*** C(15) 0.259 BOP/GDP -22.25*** C(3) 0.205 -25.93*** C(2) 0.122 InFDI/GDP -4.76*** C(12) 0.417* -14.38*** C(10) 0.102 OutFDI/GDP -20.70*** C(2) 0.594** -10.12*** C(19) 0.038 InPt/GDP -20.73*** C(3) 0.232 -6.81*** C(20) 0.029 OutPt/GDP -4.13*** C(17) 0.359* -4.13*** C(18) 0.015 TRes/IM -8.47*** C(8) 0.208 -25.74*** C(0) 0.214 Bdgt/GDP -14.88*** C(6) 0.241 -11.09*** C(7) 0.087 TMy/GDP -10.57*** C(3) 1.407*** -28.54*** C(0) 0.069 Prodty -3.83*** C(3) 0.293 -8.88*** C(11) 0.082 FD/GDP -12.99*** C(7) 0.093 -7.19*** C(3) 0.098 TTrade/GDP -10.14*** C(10) 0.334 -5.82*** C(11) 0.069
Critical values for ADF tests at 10,5 and 1% levels of significance are respectively, -2.59, -2.90 and –3.53 with a constant and –3.17, -3.48 and –4.09 with a constant and a deterministic trend. Critical values for KPSS tests at 10, 5 and 1% levels of significance are respectively, 0.35, 0.46 and 0.74 with a constant and 0.12, 0.15 and 0.22 with a constant and a linear trend. Note: For the ADF tests, the unit root null is rejected if the value of the ADF t-statistics is less than the critical value. For the KPSS tests, the null of stationarity is rejected if the value of the KPSS statistic is greater than the critical value. *, ** and *** denote statistical significance at 10, 5 and 1% level. The critical values for the ADF tests are from MacKinnon (1991).